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You Can't Become Rich In Your Pocket Until You Become Rich In Your Mind | ||||
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If the stock market is efficient, that is, if information about a stock is almost instantaneously incorporated into its priceThere are a few studies, which Ill get to later, suggesting that a moving average rule is sometimes moderately effective. Even so, however, there are several problems. One is that it can cost you a lot in commissions if the stock price hovers around the moving average and moves through it many times in both directions. Thus you have to modify the rule so that the price must move above or below its moving average by a non-trivial amount. You must also decide whether to buy at the end of the day the price exceeds the moving average or at the beginning of the next day or later still. You can mine the voluminous time-series data on stock prices to find the X that has given the best returns for adhering to the X-day moving average buy-sell rule. Or you can complicate the rule by comparing moving averages over different intervals and buying or selling when these averages cross each other. You can even adapt the idea to day trading by using X-minute moving averages defined in terms of the mathematical notion of an integral. Optimal strategies can always be found after the fact. The trick is getting something that will work in the future; everyones very good at predicting the past. This brings us to the most trenchant criticism of the moving-average strategy. If the stock market is efficient, that is, if information about a stock is almost instantaneously incorporated into its price, then any stocks future moves will be determined by random external events. Its past behavior, in particular its moving average, is irrelevant, and its future movement is unpredictable. Of course, the market may not be all that efficient. Therell be much more on this question in later chapters. Resistance and Support and All That Two other important ideas from technical analysis are resistance and support levels. The argument for them assumes that people usually remember when theyve been burned, insulted, or left out; in particular, they remember what they paid, or wish they had paid, for a stock. Assume a stock has been selling for $40 for a while and then drops to $32 before slowly rising again. The large number of people who bought it around $40 are upset and anxious to recoup their losses, so if the stock moves back up to $40, theyre likely to sell it, thereby driving the price down again. The $40 price is termed a resistance level and is considered an obstacle to further upward movement of the stock price. Likewise, investors who considered buying at $32 but did not are envious of those who did buy at that price and reaped the 25 percent returns. They are eager to get these gains, so if the stock falls back to $32, theyre likely to buy it, driving the price up again. The $32 price is termed a support level and is considered an obstacle to further downward movement. Since stocks often seem to meander between their support and resistance levels, one rule followed by technical analysts is to buy the stock when it bounces off its support level and sell it when it bumps up against its resistance level. The rule can, of course, be applied to the market as a whole, inducing investors to wait for the Dow or the S&P to definitively turn up (or down) before buying (or selling). Since chartists tend to view support levels as shaky, often temporary, floors and resistance levels as slightly stronger, but still temporary, ceilings, there is a more compelling rule involving these notions. It instructs you to buy the stock if the rising price breaks through the resistance level and to sell it if the falling price breaks through the support level. In both these cases breaking through indicates that the stock has moved out of its customary channel and the rule counsels investors to follow the new trend. As with the moving-average rules, there are a few studies that indicate that resistance-support rules sometimes lead to moderate increases in returns. Against this there remains the perhaps dispiriting efficient-market hypothesis, which maintains that past prices, trends, and resistance and support levels provide no evidence about future movements. Innumerable variants of these rules exist and they can be combined in ever more complicated ways. The resistance and support levels can change and trend up or down in a channel or with the moving average, for example, rather than remain f i xed. The rules can also be made to take account of variations in a stocks volatility as well. These variants depend on price patterns that often come equipped with amusing names. The head and shoulders pattern, for example, develops after an extended upward trend. It is comprised of three peaks, the middle and highest one being the head, and the smaller left and right ones (earlier and later ones, that is) being the shoulders. After falling below the right shoulder and breaking through the support line connecting the lows on either side of the head, the stock price has, technical chartists aver, reversed direction and a downward trend has begun, so sell. Similar metaphors describe the double-bottom trend reversal. It develops after an extended downward trend and is comprised of two successive troughs or bottoms with a small peak between them. After bouncing off the second bottom, the stock has, technical chartists again aver, reversed direction and an upward trend has begun, so buy. These are nice stories, and technical analysts tell them with great earnestness and conviction. Even if everyone told the same stories (and they dont), why should they be true? Presumably the rationale is ultimately psychological or perhaps sociological or systemic, but exactly what principles justify these beliefs? Why not triple or quadruple bottoms? Or two heads and shoulders? Or any of innumerable other equally plausible, equally risible patterns? What combination of psychological, financial, or other principles has sufficient specificity to generate effective investment rules? As with Elliott waves, scale is an issue. If we go to the level of ticks, we can find small double bottoms and little heads and tiny shoulders all over. We find them also in the movement of broad market indices. And do these patterns mean for the market as whole what they are purported to mean for individual stocks? Is the double-dip recession discussed in early 2002 simply a double bottom? Predictability and Trends I often hear people swear that they make money using the rules of technical analysis. Do they really? The answer, of course, is that they do. People make money using all sorts of strategies, including some involving tea leaves and sunspots. The real question is: Do they make more money than they would investing in a blind index fund that mimics the performance of the market as a whole? Do they achieve excess returns? Most financial theorists doubt this, but there is some tantalizing evidence for the effectiveness of momentum strategies or short-term trend-following. Economists Narasimhan Jegadeesh and Sheridan Titman, for example, have written several papers arguing that momentum strategies result in moderate excess returns and that, having done so over the years, their success is not the result of data mining. Whether this alleged profitability-many dispute it-is due to overreactions among investors or to the short-term persistence of the impact of companies earnings reports, they dont say. They do seem to point to behavioral models and psychological factors as relevant. William Brock, Josef Lakonishok, and Blake LeBaron have also found some evidence that rules based on moving averages and the notions of resistance and support are moderately effective. They focus on the simplest rules, but many argue that their results have not been replicated on new stock data. More support for the existence of technical exploitability comes from Andrew Lo, who teaches at M.I.T., and Craig MacKinlay, from the Wharton School. They argue in their book, A Non-Random Walk Down Wall Street, that in the short run overall market returns are, indeed, slightly positively correlated, much like the local weather. A hot, sunny day is a bit more likely to be followed by another one, just as a good week in the market is a bit more likely to be followed by another one. Likewise for rainy days and bad markets. Employing state-of-the-art tools, Lo and MacKinlay also claim that in the long term the prognosis changes: Individual stock prices display a slight negative correlation. Winners are a bit more likely to be losers three to five years hence and vice versa. They also bring up an interesting theoretical possibility. Weeding out some of the details, lets assume for the sake of the argument (although Lo and MacKinlay dont) that the thesis of Burton Malkiels classic book, A Random Walk Down Wall Street, is true and that the movement of the market as a whole is entirely random. Lets also assume that each stock, when its fluctuations are examined in isolation, moves randomly. Given these assumptions it would nevertheless still be possible that the price movements of, say, 5 percent of stocks accurately predict the price movements of a different 5 percent of stocks one week later. The predictability comes from cross-correlations over time between stocks. (These associations neednt be causal, but might merely be brute facts.) More concretely, lets say stock X, when looked at in isolation, fluctuates randomly from week to week, as does stock Y. Yet if Xs price this week often predicts Ys next week, this would be an exploitable opportunity and the strict random-walk hypothesis would be wrong. Unless we delved deeply into such possible cross-correlations among stocks, all we would see would be a randomly fluctuating market populated by randomly fluctuating stocks. Of course, Ive employed the typical mathematical gambit of considering an extreme case, but the example does suggest that there may be relatively simple elements of order in a market that appears to fluctuate randomly. There are other sorts of stock price anomalies that can lead to exploitable opportunities. Among the most well-known are so-called calendar effects whereby the prices of stocks, primarily small-firm stocks, rise disproportionately in January, especially during the first week of January. (The price of WCOM rose significantly in January 2001, and I was hoping this rise would repeat itself in January 2002. It didnt.) There has been some effort to explain this by citing tax law concerns that end with the close of the year, but the effect also seems to hold in countries with different tax laws. Moreover, unusual returns (good or bad) occur not only at the turn of the year, but, as Richard Thaler and others have observed, at the turn of the month, week, and day as well as before holidays. Again, poorly understood behavioral factors seem to be involved. Technical Strategies and Blackjack Most academic financial experts believe in some form of the random-walk theory and consider technical analysis almost indistinguishable from a pseudoscience whose predictions are either worthless or, at best, so barely discernibly better than chance as to be unexploitable because of transaction costs. Ive always leaned toward this view, but Ill reserve my more nuanced judgment for later in the book. In the meantime, Id like to point out a parallel between market strategies such as technical analysis in one of its many forms and blackjack strategies. (There are, of course, great differences too.) Blackjack is the only casino game of chance whose outcomes depend on past outcomes. In roulette, the previous spins of the wheel have no effect on future spins. The probability of red on the next spin is 18/38, even if red has come up on the five previous spins. The same is true with dice, which are totally lacking in memory. The probability of rolling a 7 with a pair of dice is 1/6, even if the four previous rolls have not resulted in a single 7. The probability of six reds in a row is (18/38)6; the probability of five 7s in a row is (1/6)S. Each spin and each roll are independent of the past. A game of blackjack, on the other hand, is sensitive to its past. The probability of drawing two aces in a row from a deck of cards is not (4/52 x 4/52) but rather (4/52 x 3/51). The second factor, 3/51, is the probability of choosing another ace given that the first card chosen was an ace. In the same way the probability that a card drawn from a deck will be a face card (jack, queen, or king) given that only three of the thirty cards drawn so far have been face cards is not 12/52, but a much higher 9/22. This fact-that (conditional) probabilities change according to the composition of the remaining portion of the deck-is the basis for various counting strategies in blackjack that involve keeping track of how many cards of each type have already been drawn and increasing ones bet size when the odds are (occasionally and slightly) in ones favor. Some of these strategies, followed carefully, do work. This is evidenced by the fact that some casinos supply burly guards free of charge to abruptly escort successful counting practitioners from the premises. The vast majority of people who try these strategies (or, worse, others of their own devising) lose money. It would make no sense, however, to point to the unrelenting average losses of blackjack players and maintain that this proves that there is no effective betting strategy for playing the game. Blackjack is much simpler than the stock market, of course, which depends on vastly more factors as well. as on the actions and beliefs of other investors. But the absence of conclusive evidence for the effectiveness of various investing rules, technical or otherwise, does not imply that no effective rules exist. If the markets movements are not completely random, then it has a kind of memory within it, and investing rules depending on this memory might be effective. Whether they would remain so if widely known is very dubious, but that is another matter. Interestingly, if there were an effective technical trading strategy, it wouldnt need any convincing rationale. Most in vestors would be quite pleased to use it, as most blackjack players use the standard counting strategy, without understanding why it works. With blackjack, however, there is a compelling mathematical explanation for those who care to study it. By contrast an effective technical trading strategy might be found that was beyond the comprehension not only of the people using it but of everyone. It might simply work, at least temporarily. In Platos allegory of the cave the benighted see only the shadows on the wall of the cave and not the real objects behind them that are causing the shadows. If they were really predictive, investors would be quite content with the shadows alone and would simply take the cave to be a bargain basement. The next segment is a bit of a lark. It offers a suggestive hint for developing a novel and counterintuitive investment strategy that has a bit of the feel of technical analysis. Winning Through Losing? The old joke about the store owner losing money on every sale but making it up in volume may have a kernel of truth to it. An interesting new paradox by Juan Parrondo, a Spanish physicist, brings the joke to mind. It deals with two games, each of which results in steady losses over time. When these games are played in succession in random order, however, the result is a steady gain. Bad bets strung together to produce big winnings-very strange indeed! To understand Parrondos paradox, lets switch from a financial to a spatial metaphor. Imagine you are standing on stair 0, in the middle of a very long staircase with 1,001 stairs numbered from -500 to 500 (-500, -499, -498, ..., -4, -3, -2, -1, 0, 1, 2, 3, 4, ..., 498, 499, 500). You want to go up rather than down the staircase and which direction you move depends on the outcome of coin flips. The first game-lets call it game S-is very Simple. You flip a coin and move up a stair whenever it comes up heads and down a stair whenever it comes up tails. The coin is slightly biased, however, and comes up heads 49.5 percent of the time and tails 50.5 percent. Its clear that this is not only a boring game but a losing one. If you played it long enough, you would move up and down for a while, but almost certainly you would eventually reach the bottom of the staircase. The second game-lets continue to wax poetic and call it game C-is more Complicated, so bear with me. It involves two coins, one of which, the bad one, comes up heads only 9.5 percent of the time, tails 90.5 percent. The other coin, the good one, comes up heads 74.5 percent of the time, tails 25.5 percent. As in game S, you move up a stair if the coin you flip comes up heads and you move down one if it comes up tails. But which coin do you flip? If the number of the stair youre on is a multiple of 3 (that is, . . . , -9, -6, -3, 0, 3, 6, 9, youre on is not a multiple of 3, you flip the good coin. (Note: Changing these odd percentages and constraints may affect the games outcome.) Lets go through game Cs dance steps. If you were on stair number 5, you would flip the good coin to determine your direction, whereas if you were on stair number 6, you would f l ip the bad coin. The same holds for the negatively numbered stairs. If you were on stair number -2 and playing game C, you would flip the good coin, whereas if you were on stair number -9, you would flip the bad coin. Though less obviously so than in game S, game C is also a losing game. If you played it long enough, you would almost certainly reach the bottom of the staircase eventually. Game C is a losing game because the number of the stair youre on is a multiple of 3 more often than a third of the time and thus you must flip the bad coin more often than a third of the time. Take my word for this or read the next paragraph to get a better feel for why it is. (Assume that youve just started playing game C. Since youre on stair number 0, and 0 is a multiple of 3, you would f l ip the bad coin, which lands heads with probability less than 10 percent, and you would very likely move down to stair number -1. Then, since -1 is not a multiple of 3, you would f l ip the good coin, which lands heads with probability almost 75 percent, and would probably move back up to stair 0. You may move up and down like this for a while. Occasionally, however, after the bad coin lands tails, the good coin, which lands tails almost 25 percent of the time, will land tails twice in succession, and you would move down to stair number -3, where the pattern will likely begin again. This latter downward pattern happens slightly more frequently (with probability .905 x .255 x .255) than does a rare head on the bad coin being followed by two heads on the good one (with probability .095 x .745 x .745) and your moving up three stairs as a consequence. So-called Markov chains are needed for a fuller analysis.) So far, so what? Game S is simple and results in steady movement down the staircase to the bottom, and game C is complicated and also results in steady movement down the staircase to the bottom. Parrondos fascinating discovery is that if you play these two games in succession in random order (keeping your place on the staircase as you switch between games), you will steadily ascend to the top of the staircase. Alternatively, if you play two games of S followed by two games of C followed by two games of S and so on, all the while keeping your place on the staircase as you switch between games, you will also steadily rise to the top of the staircase. (You might want to look up M. C. Eschers para doxical drawing, Ascending and Descending for a nice visual analog to Parrondos paradox.) Standard stock-market investments cannot be modeled by games of this type, but variations of these games might conceivably give rise to counterintuitive investment strategies. The probabilities might be achieved, for example, by complicated combinations of various financial instruments (options, derivatives, and so on), but the decision which coin (which investment, that is) to flip (to make) in game C above would, it seems, have to depend upon something other than whether ones holdings were worth a multiple of $3.00 (or a multiple of $3,000.00). Perhaps the decision could depend in some way on the cross-correlation between a pair of stocks or turn on the value of some index being a multiple of 3. If strategies like this could be made to work, they would yield what one day might be referred to as Parrondo profits. Finally, lets consider a companion paradox of sorts that might be called losing through winning and that may help explain why companies often overpaid for small companies they were purchasing during the bubble in the late 90s. Professor Martin Shubik has regularly auctioned off $1 to students in his classes at Yale. The bidding takes place at f i fty-four intervals, and the highest bidder gets the dollar, of course, but the second highest bidder is required to pay his bid as well. Thus, if the highest bid is 504 and you are second highest at 454, the leader stands to make 504 on the deal and you stand to lose 454 on it if bidding stops. You have an incentive to up your bid to at least 554, but after youve done so the other bidder has an even bigger incentive to raise his bid as well. In this way a one dollar bill can be successfully auctioned off for two, three, four, or more dollars. If several companies are bidding on a small company and the cost of the preliminary legal, financial, and psychological efforts required to purchase the company are a reasonable fraction of the cost of the company, the situation is formally similar to Shubiks auction. One or more of the bidding companies might feel compelled to make an exorbitant preemptive offer to avoid the fate of the losing bidder on the $1. WorldComs purchase of the web-hosting company Digex in 2000 for $6 billion was, I suspect, such an offer. John Sidgmore, the CEO who succeeded Bernie Ebbers, says that Digex was worth no more than $50 million, but that Ebbers was obsessed with beating out Global Crossing for the company. The purchase is much more bizarre than Parrondos paradox. |
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